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The Luna Bound Propagator for Formal Analysis of Neural Networks

arXiv cs.LG / 3/26/2026

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Key Points

  • The paper introduces Luna, a new C++ bound propagator aimed at formal neural network verification, addressing alpha-CROWN’s prior limitation to Python implementations.
  • Luna supports multiple verification-relevant techniques—Interval Bound Propagation, CROWN, and alpha-CROWN—while operating over a general computational graph.
  • The authors present Luna’s architecture and evaluate it using VNN-COMP 2025 benchmarks, focusing on bound tightness and computational efficiency.
  • Results indicate Luna is competitive with the state-of-the-art alpha-CROWN implementation in both effectiveness and runtime performance, suggesting practical viability for production DNN verifiers.

Abstract

The parameterized CROWN analysis, a.k.a., alpha-CROWN, has emerged as a practically successful bound propagation method for neural network verification. However, existing implementations of alpha-CROWN are limited to Python, which complicates integration into existing DNN verifiers and long-term production-level systems. We introduce Luna, a new bound propagator implemented in C++. Luna supports Interval Bound Propagation, the CROWN analysis, and the alpha-CROWN analysis over a general computational graph. We describe the architecture of Luna and show that it is competitive with the state-of-the-art alpha-CROWN implementation in terms of both bound tightness and computational efficiency on benchmarks from VNN-COMP 2025.

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